Power system state estimation is an important prerequisite function for the intelligent management of a power grid. Two types of state estimators, namely, static and dynamic are possible for realization. Traditionally, static state estimation techniques are used by the electric power industry to estimate the state (typically the magnitudes and angles of the bus voltage phasors) of power transmission and distribution systems, due to the techniques' relative simplicity and the ready availability of supervisory control and data acquisition (SCADA) data that is often obtained at relatively slow sampling rates. Dynamic state estimation, on the other hand, would allow power system operators to observe and respond to transient state changes in the power system, and is likely to become more relevant with the increasing availability of fast-sampled sensor data, such as phasor measurement unit (PMU) data.
State estimation is typically executed on the entire power grid, under the jurisdiction of the governing and/or monitoring entity, e.g., an independent system operator (ISO) or utility, while considering all the components and their interactions in the grid at once. However, several changes in the power industry are resulting in systems where a single governing and/or monitoring entity may no longer have immediate access to measurements across the entire grid. For instance, in some cases operational control of transmission grids is performed by an entity other than the generating/distributing utilities. These changes, coupled with the rapidly increasing complexity of power systems, create challenges for power system state estimation. Accordingly, improved techniques for power system state estimation are needed.